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Can you share an example of how your team overcame an AI-related challenge through human ingenuity?
Can you share an example of how your team overcame an AI-related challenge through human ingenuity?
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2 answers
Priyanka K
Software Engineer
29
Answers
Little Elm, Texas
Updated
Priyanka’s Answer
Certainly! Let’s explore an example of how human ingenuity can solve challenges posed by AI systems—this can apply in various real-world scenarios. Here’s a hypothetical but realistic example based on common experiences with AI deployment:
Scenario: AI Misinterprets Customer Sentiment in a Chatbot
Challenge:
An AI-powered chatbot designed to handle customer service inquiries was frequently misinterpreting customer sentiment. For instance, when a customer typed, "I'm so thrilled to finally get this resolved," the chatbot would incorrectly classify it as a negative sentiment because the sentence contained the word "resolved," which was often paired with complaints in the training data. As a result, the chatbot would escalate the issue unnecessarily to a human agent.
Human Ingenuity to the Rescue:
The team combined human observation, creativity, and problem-solving to address the problem:
Step 1: Identifying the Root Cause
A cross-functional team of engineers, linguists, and customer service experts manually reviewed conversations flagged as problematic by the chatbot. They noticed that the sentiment analysis AI was overly reliant on keywords without fully understanding context or tone.
Insight: Humans realized that the issue wasn’t just about training data but about the need for the AI to interpret combinations of words and punctuation with nuance (e.g., "thrilled" + "finally" is positive).
Step 2: Designing a Hybrid Approach
Instead of relying solely on AI, the team implemented a hybrid model:
They added a secondary layer of human oversight for flagged conversations.
A team of linguistics experts augmented the training data with edge cases, including diverse sentence structures, emojis, and punctuation styles.
The chatbot was also programmed to ask clarifying questions in ambiguous situations, like: “It sounds like you’re happy with the resolution—did I get that right?”
Step 3: Leveraging Creativity in Training
The team used creativity to simulate real-world conversations:
They designed mock chats with varied emotional tones.
They incorporated slang, cultural phrases, and regional nuances, which AI often struggles with.
They ran workshops where customer service representatives role-played interactions to create new training data.
Step 4: Continuous Feedback Loop
To ensure the problem didn’t recur, they implemented a feedback mechanism:
Human agents flagged future cases where the AI misinterpreted sentiment, feeding this data back into the system for retraining.
The Outcome:
The chatbot’s sentiment analysis accuracy increased by over 90%.
Escalations to human agents dropped by 40%, saving time and resources.
Customer satisfaction scores improved as the chatbot responses became more empathetic and contextually accurate.
Key Takeaway:
This example highlights that while AI is powerful, it’s not infallible. Human ingenuity—through observation, collaboration, and creative problem-solving—can bridge gaps in AI systems. It’s a reminder that AI works best as a tool to augment human intelligence, not replace it. Together, humans and AI can achieve smarter, more nuanced solutions.
Scenario: AI Misinterprets Customer Sentiment in a Chatbot
Challenge:
An AI-powered chatbot designed to handle customer service inquiries was frequently misinterpreting customer sentiment. For instance, when a customer typed, "I'm so thrilled to finally get this resolved," the chatbot would incorrectly classify it as a negative sentiment because the sentence contained the word "resolved," which was often paired with complaints in the training data. As a result, the chatbot would escalate the issue unnecessarily to a human agent.
Human Ingenuity to the Rescue:
The team combined human observation, creativity, and problem-solving to address the problem:
Step 1: Identifying the Root Cause
A cross-functional team of engineers, linguists, and customer service experts manually reviewed conversations flagged as problematic by the chatbot. They noticed that the sentiment analysis AI was overly reliant on keywords without fully understanding context or tone.
Insight: Humans realized that the issue wasn’t just about training data but about the need for the AI to interpret combinations of words and punctuation with nuance (e.g., "thrilled" + "finally" is positive).
Step 2: Designing a Hybrid Approach
Instead of relying solely on AI, the team implemented a hybrid model:
They added a secondary layer of human oversight for flagged conversations.
A team of linguistics experts augmented the training data with edge cases, including diverse sentence structures, emojis, and punctuation styles.
The chatbot was also programmed to ask clarifying questions in ambiguous situations, like: “It sounds like you’re happy with the resolution—did I get that right?”
Step 3: Leveraging Creativity in Training
The team used creativity to simulate real-world conversations:
They designed mock chats with varied emotional tones.
They incorporated slang, cultural phrases, and regional nuances, which AI often struggles with.
They ran workshops where customer service representatives role-played interactions to create new training data.
Step 4: Continuous Feedback Loop
To ensure the problem didn’t recur, they implemented a feedback mechanism:
Human agents flagged future cases where the AI misinterpreted sentiment, feeding this data back into the system for retraining.
The Outcome:
The chatbot’s sentiment analysis accuracy increased by over 90%.
Escalations to human agents dropped by 40%, saving time and resources.
Customer satisfaction scores improved as the chatbot responses became more empathetic and contextually accurate.
Key Takeaway:
This example highlights that while AI is powerful, it’s not infallible. Human ingenuity—through observation, collaboration, and creative problem-solving—can bridge gaps in AI systems. It’s a reminder that AI works best as a tool to augment human intelligence, not replace it. Together, humans and AI can achieve smarter, more nuanced solutions.
Michael Rill
Program Management
5
Answers
Redmond, Washington
Updated
Michael’s Answer
Right now, there's a big change in how teams create AI products. In the past, making software started with a detailed plan that engineers would follow. But with AI, that approach doesn't quite fit. Instead of just writing plans, teams now focus a lot on creating evaluations.
An evaluation is like a test to see if the AI is working as people expect. Instead of just making sure a button works or a file saves, we ask: did the AI give a helpful answer? Did it solve the problem in a natural and trustworthy way?
Here are some examples:
For a writing assistant: does the AI make a sentence clearer without changing its meaning?
For a math helper: does it not only find the right answer but also show steps a student can follow?
For a business tool: does it summarize a meeting well enough that someone who missed it feels informed?
These questions can't be graded automatically. They rely on judgment, subtlety, and context. Right now, evaluations need experts—people who know what "good" looks like and can explain tricky situations. AI models get better because humans guide them with their expertise.
This is where human creativity shines. Our team found that the best results happen when experts design evaluations that match what customers really want, not just what's technically correct. It's a partnership: the model brings speed and scale, while humans bring the wisdom to judge quality.
So, AI isn't replacing human work here; it's enhancing it. The evaluations we create today are possible because humans and AI work together, each doing what they do best.
An evaluation is like a test to see if the AI is working as people expect. Instead of just making sure a button works or a file saves, we ask: did the AI give a helpful answer? Did it solve the problem in a natural and trustworthy way?
Here are some examples:
For a writing assistant: does the AI make a sentence clearer without changing its meaning?
For a math helper: does it not only find the right answer but also show steps a student can follow?
For a business tool: does it summarize a meeting well enough that someone who missed it feels informed?
These questions can't be graded automatically. They rely on judgment, subtlety, and context. Right now, evaluations need experts—people who know what "good" looks like and can explain tricky situations. AI models get better because humans guide them with their expertise.
This is where human creativity shines. Our team found that the best results happen when experts design evaluations that match what customers really want, not just what's technically correct. It's a partnership: the model brings speed and scale, while humans bring the wisdom to judge quality.
So, AI isn't replacing human work here; it's enhancing it. The evaluations we create today are possible because humans and AI work together, each doing what they do best.